Systems and/or methods for caching xml information sets with delayed node instantiation

ABSTRACT

Certain example embodiments described herein relate to techniques for processing XML documents of potentially very large sizes. For instance, certain example embodiments parse a potentially large XML document, store the parsed data and some associated metadata in multiple independent blocks or partitions, and instantiate only the particular object model object requested by a program. By including logical references rather than physical memory addresses in such pre-parsed partitions, certain example embodiments make it possible to move the partitions through a caching storage hierarchy without necessarily having to adjust or encode memory references, thereby advantageously enabling dynamic usage of the created partitions and making it possible to cache an arbitrarily large document while consuming a limited amount of program memory.

TECHNICAL FIELD

Certain example embodiments described herein relate to techniques for processing XML documents of varying sizes. More particularly, certain example embodiments relate to techniques that parse a potentially large XML document, store the parsed data and some associated metadata in multiple independent blocks or partitions, and instantiate only the particular object model object requested by a program.

BACKGROUND AND SUMMARY OF EXAMPLE EMBODIMENTS OF THE INVENTION

XML is a known document encoding standard that facilitates the exchange of information between computer systems. Among other things, XML prescribes a standard way of encoding named hierarchical information.

XML documents can be processed in many ways. However, one common processing technique involves extracting XML documents' information content and creating a memory-resident representation of that information. One commonly used model is the DOM or the Document Object Model, which is governed by a W3C standard. Each component in an XML document is represented by a discrete DOM object.

One drawback of typical DOM implementations is that the entire object model must reside in memory. Although this approach works well for smaller XML documents (e.g., that can be loaded in memory or a reasonably sized virtual memory), handling very large documents in this way can become cumbersome. For instance, loading large documents may result in high virtual memory demand and poor system performance. This approach also effectively places an upper limit on the size of any XML document that can be processed that depends, for example, on the amount of available memory (real and/or virtual). Additionally, for environments that use dynamic allocation (such as, for example, Java-based environments), this situation results in large numbers of discrete heap-resident objects, which can potentially adversely affect the performance of memory recovery for the objects when they are no longer in use (e.g., through garbage collection). Those skilled in the art know that system performance often degrades sharply when document size becomes unmanageable.

Yet another drawback of the memory-resident approach is that it can be very difficult to share a parsed document in a federated system where processes may not have convenient access to shared memory.

Of course, it would be desirable to implement an approach to XML parsing that performs consistently well under varying circumstances such as, for example, simultaneously processing a small number of very large documents, simultaneously processing a large number of small documents, and/or the like. The need to meet these desires becomes yet more important as the system scales up to an Enterprise-class server sized system.

The inventor has observed that solutions to the XML memory problem tend to fall into one of three categories, namely, adapting the application to an existing DOM model in some pragmatic way, using some model other than DOM, or implementing an improved DOM implementation that improves on prior implementations.

Pragmatic adaptation to DOM oftentimes includes allocating large amounts of memory to the process and simply tolerating poor performance; designing or redesigning the application to limit the size of the document; and/or resorting to some type of ad-hoc partitioning of the documents. Some products currently available by the assignee of the instant application employ the ad-hoc partitioning approach for processing very large documents. For example, the assignee's Integration Server provides an iterating parser that generally makes it possible to maintain a parsing window that typically avoids loading the entire document, subject to certain constraints of course. As another example, the assignee's Trading Networks decomposes large documents into smaller documents in a very specialized approach. It is noted that adapting an application to use ad-hoc partitioning can be very difficult, depending on the size, complexity, processing requirements, and other features of the application and/or environment. Similarly, obtaining maximum performance using ad-hoc partitioning also can be difficult.

Using models other than DOM typically involves a streaming approach in which information is processed during one pass of the document. A streaming push model, such as SAX, can be very efficient from a performance point of view. Unfortunately, however, such models oftentimes are difficult to program. A streaming pull model, such as the Axiom model used by Axis, is easier to use in many situations, but still does not lend itself well to situations that require non-document-order processing. If it is not possible to process data in document-order, the user generally must enable Axiom caching, which stores processed data in a cache so that it may be subsequently reused. This cache, however, is a memory-resident pool of objects and, as a result, its behavior can still degrade into the DOM-like pattern, depending on the usage pattern. The current Integration Server product requires that the entire model be memory resident in order to convert the XML document into an Integration Server Document, so the streaming approach does not improve native Document processing as greatly as is desirable.

Another non-DOM implementation is Ximpleware's VTD-XML. See, for example, U.S. Pat. No. 7,761,459, the entire contents of which are hereby incorporated herein by reference. This implementation is believed to completely avoid the creation of DOM objects. It instead uses a location cache (e.g., a Binary File Mask or BFM) to maintain information about the document that resides somewhere else in memory or on disk. The VTP API apparently allows a program to access the document contents from a combination of information in the BFM and the original document without requiring object instantiation. Ximpleware claims that this approach significantly improves performance. Yet there are drawbacks associated with this approach. For example, many third-party products are designed to work with the DOM API. Without the DOM API, this approach is a programming island, requiring custom programming for each application. Moreover, although the '459 patent provides for updates (e.g., adding new components) by allocating empty records in the Binary File Mask, there is no description is provided for the circumstances under which the empty records become filled, or how performance might be affected by a large number of insertions into the original document.

Other non-DOM approaches include customized applications that do not directly utilize DOM. For example, U.S. Pat. No. 8,131,728 (apparently assigned to IBM), which is hereby incorporated herein by reference in its entirety, describes a technique for extracting the structural information from an XML document and encoding the structural information as a memory-resident index with indexes into the original source data document. The application, a high-speed utility for loading and unloading a Configuration Management Database, processes the smaller memory-resident index rather than the larger source document. Unfortunately, however, the '728 patent (e.g., at col. 5, lines 7-23) suggests that the index is memory-resident, which effectively limits the ultimate size of the document that can be processed, and/or the number of documents that can be processed concurrently due to the total memory occupied by the index.

Other attempts have been made in the pursuit of an improved DOM implementation. The Apache foundation's Xerces DOM parser, for example, is widely used throughout the industry. This product makes use of deferred object instantiation, but unfortunately does not provide a caching mechanism to facilitate processing of documents whose memory model exceeds the amount of available memory.

It is believed that neither Xerces nor Axiom provides the ability to process arbitrarily large documents in a random fashion. And while Ximpleware VDT-XML can process arbitrarily large documents, it does so using a non-standard (e.g., non-DOM) API.

None of these approaches explicitly describe a systematic technique for limiting the total memory allocation for the Document processing within a system. More generally, there do not seem to be any apparent facilities for systematic tuning of system performance.

In addition to the above-identified issues with the conventional approaches discussed above, it is believed that none of these approaches addresses the issue of sharing a large parsed document if a session migrates across processor sessions. Furthermore, it is believed that none of these approaches addresses the issues of scalability and predictability for Enterprise-class and/or other large scale servers. There is no express explanation in the above-identified approaches tending to show that shared and/or distributed processing can be accommodated.

Thus, it will be appreciated by those skilled in the art that there is need for improved techniques for processing large XML documents, e.g., in ways that overcome the above-described and/or other problems.

In certain example embodiments, a system for processing XML documents is provided. Processing resources include at least one processor, a memory, and a non-transitory computer readable storage medium. The processing resources are configured to: parse an XML document into one or more constituent nodes, with the XML document including a plurality of objects representable in accordance with an object model, and with the XML document being parsed without also instantiating the objects therein; store the parsed constituent nodes and associated metadata in one or more partitions; and in response to requests for objects from the XML document from a user program, instantiate only said requested objects from their associated partition(s) in accordance with the object model.

In certain example embodiments, a method of processing large documents is provided. In connection with at least one processor, a large document is parsed into one or more constituent nodes, with the document including a plurality of objects representable in accordance with an object model, and with the document being parsed without also instantiating the objects therein. The parsed constituent nodes and associated metadata are stored in one or more cacheable partitions, with the cacheable partitions being located in a memory and/or a non-transitory backing store. A request from a user program for an object from the document is handled by: identifying the partition(s) in which nodes corresponding to the requested object is/are located, and instantiating only said requested objects from the identified partition(s) in accordance with the object model. The cacheable partitions are structured to include only logical references among and between different nodes.

In certain example embodiments, there is provided a non-transitory computer readable storage medium tangibly storing instructions that, when executed by at least one processor of a system, perform a method as described herein.

According to certain example embodiments, each said cacheable partition may include a locator array, a properties array, and a character array. The locator array may be configured to identify starting positions of nodes encoded in the properties array. The properties array may be configured to store encoded nodes, as well as, for each said encoded node: metadata including a respective node type, reference(s) to any familial nodes thereof, and offset(s) into the character array for any attribute and/or text value(s) associated therewith.

According to certain example embodiments, the XML document may be parsed by executing a pre-parsing initialization process that includes: creating a cacheable document node that corresponds to the starting point for user program access to the XML document; allocating a name dictionary that includes an indexed entry for each unique XML tag name included in the XML document; allocating a namespace dictionary that includes an index entry for each unique XML namespace included in the XML document; and allocating a partition table that includes a list of cacheable partitions and an allocation array that allows a node's allocation identifier to be resolved to a specific cacheable partition, each said cacheable partition including metadata from the pre-parsing initialization process.

According to certain example embodiments, the parsing may include: recognizing parsing events of predefined parsing event types within the XML document; creating nodes for the recognized parsing events; adding the created nodes to a current partition while there is sufficient space therein; and creating a new partition when there is insufficient space in the current partition for adding created nodes, updating the partition table, and continuing with the adding by treating the newly created partition as the current partition.

A feature of certain example embodiments is that the partitions may be movable from the memory to the non-transitory computer readable storage medium when memory usage reaches or exceeds a threshold.

Another feature of certain example embodiments is that the partitions may be movable through a caching storage hierarchy of the processing resources without adjusting or encoding memory references therein.

Another feature of certain example embodiments is that objects instantiated from the partitions, when resident in the memory, may be free from references to other objects in their own respective partitions and any other partitions.

Still another feature of certain example embodiments is that partitions for the parsed document may be removed from the memory and/or the non-transitory computer readable storage medium when the user program no longer includes any references to the document or any objects thereof.

Still another feature of certain example embodiments is that the partitions may include only logical references among and between different nodes.

These aspects, features, and example embodiments may be used separately and/or applied in various combinations to achieve yet further embodiments of this invention.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages may be better and more completely understood by reference to the following detailed description of exemplary illustrative embodiments in conjunction with the drawings, of which:

FIG. 1 is a schematic view that demonstrates user program creation of cached XML documents in accordance with certain example embodiments;

FIG. 2 demonstrates user navigation of a cached document, in accordance with certain example embodiments;

FIG. 3 is a flowchart showing an example initialization process for the partitioning process, in accordance with certain example embodiments;

FIG. 4 is a flowchart showing an example partition process in accordance with certain example embodiments;

FIG. 5 schematically shows the contents of an example cacheable partition created by a partitioning engine in accordance with certain example embodiments; and

FIG. 6 schematically shows partition tables organizing collections of cacheable partitions for cacheable documents in the parsed data store in accordance with certain example embodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

Certain example embodiments relate to DOM implementations that improve large XML documents processing. The techniques of certain example embodiments do not create the entire object model. Instead, they may completely parse the XML document, storing the parsed data and some metadata in multiple independent blocks, hereafter referred to as partitions. The techniques of certain example embodiments may, in addition or in the alternative, instantiate (e.g. create) only the particular DOM object requested by a program. Employing these techniques alone and/or in combination advantageous reduces the number of memory-resident structures needed to represent an XML document, thereby making it possible to efficiently process very large documents without consuming large amounts of memory.

It will be appreciated, then, that certain example embodiments involve the pre-parsing of an XML document into independent partitions that include the parsed data for the XML document, without necessarily creating the associated DOM objects. Theses partitions may include logical references rather than physical memory addresses in certain example embodiments, and the partitions can be moved through a caching storage hierarchy without necessarily having to adjust or encode memory references. This design approach advantageously enables dynamic usage of the created partitions. Unlike many conventional approaches, the partitions of certain example embodiments are not necessarily bound to a static memory address, a database temporary table, or even a particular process. This makes it possible to cache an arbitrarily large document while consuming a limited amount of program memory. Additionally, the DOM objects instantiated from any of the partitions, when memory-resident, may lack all references to objects in its own or any other partitions, thereby making each partition independent even when memory-resident. As a consequence, operating environments that use a dynamic heap (such as, for example, Java-based environments) automatically gain the additional benefit of automatic memory reclamation for partitions that are not in use (e.g., referenced). Because it is possible to share a disk-backed cache (such as, for example, Terracotta's Big Memory cache utilizing the Terracotta Server Array), it becomes possible to access a parsed document from a programming session that migrates between computer systems (e.g., that have a shared disk cache).

Certain example embodiments are advantageous in the sense that the object model becomes naturally updatable. The parsed data allocation scheme of certain example embodiments does not require that logically adjacent content be physically adjacent in a partition. For instance, a newly allocated node (as well as text and/or attribute values) can be physically added the end of the last cacheable partition. References from pre-existing nodes in other partitions to the newly created node may be established using the allocation identifier of the newly encoded node.

The systematic throttling of memory consumption advantageously leads to stable and predictable system operation when processing large documents or large numbers of documents.

Certain example embodiments involve an XML partitioning engine, a parsed data store, and a DOM instantiator. The XML partitioning engine may be implemented as a program that reads an XML document stream and converts it into a series of encoded nodes in cacheable partitions that contain no memory references (e.g., addresses). The parsed data store may be implemented as a program and/or repository that stores and retrieves the cacheable partitions outside of the user program's memory space (for example, in Java, outside of the memory heap). The DOM instantiator may be implemented as a program that creates DOM objects on demand. It may accomplish this by retrieving the appropriate cacheable partition from a local partition pool or the parsed data store, and creating the DOM object. For example, in Java, it may construct an appropriate W3C DOM node. Further details of these example components will be provided below in connection with an example implementation.

FIG. 1 is a schematic view that demonstrates user program creation of cached XML documents in accordance with certain example embodiments. The user program may initiate document creation by invoking the XML partitioning engine 102, e.g., with the source document XML stream 104 and a parsed data store implementation 106 as inputs. The XML document stream 104 may be thought of as the stream of characters comprising the XML document. The parsed data store 106, on the other hand, may provide the mechanism for storing the encoded document outside of the program's memory space.

The XML partitioning engine 102 may return a standard W3C DOM document node implementation that includes all of the information needed to construct DOM objects 108 from the parsed document. The user program may employ standard DOM methods to navigate the parsed document. Each navigation request may retrieve associated backing information, e.g., from the parsed data store 106 or the local partition pool, and instantiate (e.g., create) the requested object. As will be explained in greater detail below, a partition table 110 may include a list of cacheable partitions. The newly created partitions 112 may be stored in the now-updated parsed data store 106′.

When the user program no longer includes any references to the document node or any nodes related to it, the backing cacheable partitions for the parsed document may be removed from the parsed data store 106. For example, in Java, the finalizer method may be invoked on the documents when there are no references to the document and, thus, the backing partitions can be removed from the parsed data store 106.

FIG. 2 demonstrates user navigation of a cached document, in accordance with certain example embodiments. As can be seen, navigation requests are processed and used to retrieve nodes from local partition(s) and/or the parsed data store, and from a programmer's point of view, externally, the document does not “look” any different from a typical DOM structure. However, there are differences internally, e.g., in the structure of the document and how the tree structure is managed as set forth in greater detail below. The final node in the chain may be used to convey an “end of program” or “end of processing” message back to the user. In some cases (for example in some garbage-collected runtimes such as Java-based environments), such an explicit “end” message may not necessary, as a garbage collector may simply operate on the structure automatically. For example, in some cases, the Java implementation may receive a call-back, indicating that the buffers in the parsed document store are no longer in use.

As indicated above, the XML partitioning engine is configured to receive an XML input stream and/or a parsed data store entry as input. It may, as output, produce encoded XML fragments (that may be stored in the parsed data store) and a DOM cacheable document node (e.g., for program's memory space). FIG. 3 is a flowchart showing an example initialization process for the partitioning process, in accordance with certain example embodiments. A new cacheable document node is created in step S302, which may be, for example, a W3C Document Node that serves as the starting point for user program access. A name dictionary is allocated in step S304, with the name dictionary including one entry for each unique XML tag name that appears in the document. The dictionary may be structured as a sequential list, and items in the dictionary may be referenced by an integer or other index. This structure can in some instances result in considerable memory savings for large document, e.g., when there are many occurrence of a given tag name, and especially when the names are long. The name dictionary may be empty initially and may be implemented in fashion to expedite the test for uniqueness. For example, a binary search or hash table may be used. A namespace dictionary is allocated in step S306. The Namespace Dictionary may include one entry for each unique XML namespace that appears in the document and, similar to the above, the namespace dictionary may be implemented as a sequential list, with items in the dictionary being referenced by an integer or other index. Initially, the namespace dictionary may be empty. The namespace dictionary may be implemented in fashion to expedite the test for uniqueness, and binary searching or hash tables may be used to facilitate this behavior in certain example embodiments.

Optionally, an attribute value dictionary may be allocated in step S308. When it is used, unique occurrences of attribute values may be saved to this location, potentially reducing memory usage. Its implementation may be similar to the names and namespace dictionaries. In addition, or in the alternative, a text value dictionary optionally may be allocated in step S310. When it is used, unique occurrences of text values may be saved here, potentially reducing memory usage. Its implementation may be similar to the names and namespace dictionaries.

A partition table allocated is in step S312. As alluded to above, the partition table may include a list of cacheable partitions, wherein each cacheable partition includes the information from the pre-parsed XML document. Initially, the partition table is empty. It may include, for example, an allocation array that records the allocation identifier of the last encoded node in a partition. This may make it is possible to determine which cacheable partition includes a given node without retrieving the partition if it has been archived to the parsed data store.

FIG. 4 is a flowchart showing an example partition process in accordance with certain example embodiments. As shown in step S402, the partition table is initialized (e.g., in accordance with the example process described above in connection with FIG. 3), and the XML document stream is opened. The cacheable document node created in step S302 becomes the current parent node and all subsequently encoded nodes are descendent children of the document node. A streaming XML tokenizer processes an XML document fragment, responding to a sequence of XML parsing events such as the start of an XML element, the declaration of an XML namespace, the presence of an XML attribute, the presence of character data, the presence of a comment, etc. Accordingly, as shown in step S404, the next XML fragment is obtained until processing of the document is complete. As the parsing events are recognized, each event is used to move associated parsed data (if the event has associated data) into a cacheable partition. In step S406, the parsing event determines the amount of space that is needed in the current cacheable partition. If there is insufficient space (e.g., as determined in step S408), a new cacheable partition is allocated and added to the partition table (e.g., in step S410).

If there is enough space in the current cacheable partition, or once a new cacheable partition is created and the partition table is updated accordingly, the node is encoded into the appropriate cacheable partition in step S412. In brief, its familial references may be updated, and the new node may become the offspring of the current parent. When a node is encoded into a partition, its position in the property array may be recorded in the locator array so that it is possible to quickly find the position of an encoded node given its allocation identifier. Each partition may have a structure that supports this functionality. For instance, each partition may include a character array where XML text and XML attribute values are copied, a properties array where the various properties of the XML event are encoded and, a locator array that sequentially records the position in the properties array where a node's properties are stored. The properties of the XML event may include information such as, for example, the type of node, the allocation identifiers of its parent, its sibling and first and last child nodes, references to the character array where the event's textual information is stored, etc.

In certain example embodiments, the parsed data for each event type at step S412 may be written as follows. When an element start tag event is processed, the current position in the properties array is recorded in the location array (e.g., so that the start of the element in the properties array can be identified accordingly), and an element code is written to the properties array, along with an integer or other index into the name dictionary. When an element's namespace declaration is processed, an index is written to the properties array, along with a reference to the appropriate namespace dictionary entry. When an attribute is encountered, an index is written to the properties array, along with a reference to the appropriate name dictionary entry. The attribute value may be copied to the character array, and the location of the attribute value in the character array may be copied into the properties array following the attribute name index. When a text event is received, a text code may be written to the properties buffer. The text content may be copied to the character array, and the position in the character array may be saved in the properties array. When a comment event is received, a comment code may be written to the properties array, followed by an index being written into the character array. The comment content may be copied into the Character Array. After the data for an event has been encoded (e.g. in step S412), additional special processing may occur for the element start and element end events. When an element start is processed (e.g., as indicated in step S416), the allocation identifier for the current parent node is saved (e.g., by pushing it onto a LIFO stack). The allocation identifier of the just encoded element becomes the new current parent node. All subsequently allocated nodes become children of the new current parent node. When an element end tag is processed (e.g., as indicated in step S420), the current parent node is restored to its previous value (e.g., by popping it from a LIFO stack). All subsequently allocated nodes become children of the previous parent node. When no more XML fragments are available at step S404, the document has been completely processed.

In certain example embodiments, an optional throttling mechanism may be used to limit the total amount of program storage that is allocated to the local partition pool. This may in some instances make it possible to dampen the effects of an atypically large number of documents arriving during a short interval, while reducing the likelihood of insufficient memory errors.

As alluded to above, when the current cacheable partition becomes full, a new cacheable partition may be allocated. If the local partition pool also is full, the cacheable partition may be archived to the parsed data store. In order to transfer a Cacheable Document intact to a different session, cacheable partitions in the local partition pool may be archived to the parsed data store. Additionally, the partition table itself, which may not normally be archived, may also be committed to the parsed data store. If the archival storage system supports direct access to archived data (e.g., without full serialization and deserialization), it also may be possible to curtail the use of the local partition pool in favor of direct access to the archived data. Thus, there may be no inherent limit on the number and size of partitions, other than the total available program memory, cache, disk storage, etc.

FIG. 5 schematically shows the contents of an example cacheable partition created by a partitioning engine in accordance with certain example embodiments and, thus, shows an example encoding for the nodes. The cacheable partition 502 shown in FIG. 5 includes a locator array 504, a properties array 506, and a character array 508. The inclusion of the locator array 504 indicates that certain example embodiments involve locator information being stored in each partition (e.g., as opposed to retaining a single completely memory-resident index). Plural encoded node entries may be present in the properties array 506. The example encoded node entry 510 shown in FIG. 5 may include a variety of data and/or linkages to data. For example, nodes may have an entry for DOM node type. Additionally, there may be references 512 (allocation identifiers) for parent, previous sibling, and next sibling.

Text nodes may have an offset into the character array. If a dictionary is used for text values, then an offset into the text value dictionary may be used instead of an offset into the character array. Comment nodes and processing instruction nodes may have offsets into the character array 508.

Elements may have an offset into the names dictionary 514 and, if the namespace is qualified, they also may have an offset into the namespace dictionary 516. There also may be a list of entries, e.g., with one entry for each attribute. Each entry may include an offset into the names dictionary 514, an offset into the namespace dictionary 516 (if needed), and an offset into the character array 508 of the partition table. If the optional attribute values dictionary is in use, an offset into the attribute values dictionary may be saved instead of an offset into the character array 508. Additionally, elements may have allocation identifier references to their first and last child nodes.

As indicated above, and as can be appreciated from the description of FIG. 5, certain example embodiments do not retain original documents or references to their contents. In certain example embodiments, there is no encoding for an element end because all familial references may be fully realized as allocation identifiers in the encodings rather than being computed based on context. In other words, familial relationships between nodes may be represented using the allocation identifier uniquely assigned to each node.

Instantiated DOM nodes may reference familial nodes (e.g., parent, sibling, and/or child nodes) using the familial node's allocation identifier. Thus, in certain example embodiments, the XML instantiator may find the referenced node in the appropriate cacheable partition by searching the allocation array. After locating the appropriate cacheable partition, the allocation identifier may be used as an offset into the partition's locator array, e.g., to determine the position of the encoded node information in the properties array. The information may be used to construct a DOM node, and the node may be returned to the requesting program.

Because these DOM nodes may not include any address references to other DOM nodes, the nodes may be eligible for deallocation (or garbage collection) when they are no longer in use. By contrast, in a traditional DOM tree, retaining a reference to any node in the tree retains the entire tree.

The parsed data store may be thought of as at least partly being an off-program-memory bag that is used to archive cacheable partitions. The parsed data store does not occupy storage in the user program's space in certain example embodiments and thus makes it possible for a user program to access an arbitrarily large XML document, in some instances constrained only by the amount of storage accessible to the parsed data store.

The overall structure of an example parsed data store is shown in FIG. 6. That is, FIG. 6 schematically shows partition tables organizing collections of cacheable partitions for cacheable documents in the parsed data store in accordance with certain example embodiments. In the FIG. 6 example, first and second partition tables 602 a-b store respective allocation arrays 604 a-b. The allocation arrays 604 a-b include references to cacheable partitions, e.g., of the sort described above in connection with FIG. 5. It will be appreciated from the FIG. 6 diagram that partitions from multiple documents can be stored in a single given store, e.g., since there is no connection to one of the cacheable partitions. However, in some example embodiments, it is possible to have a one-to-one relationship as between partition stores and documents.

Certain example embodiments may use Terracotta Big Memory caching to store the cacheable partitions in off-heap memory. See, for example, U.S. application Ser. No. 13/354,892, filed on Jan. 20, 2012, for a description of such example techniques. The entire contents of this application are hereby incorporated herein by reference.

When it comes to updating cached documents, certain example embodiments may not include an explicit update mode for cached documents, since cached documents are implicitly updatable via the DOM API. Updating an encoded node simply involves replacing existing references in the encode node with new references. From a user's perspective, instead of being driven by parsing events, the node creation methods in the DOM API may in certain example implementations directly call the node encoding program to append new encodings to the cached partitions. These appended encodings may be physically adjacent to one another, but their logical positions may be anywhere in the document, thereby fully supporting the ability to insert new child nodes and/or remove existing child nodes, at any point in the tree.

In certain example embodiments, by default, text and attribute values may be copied “as is” into each cacheable partition's character array. However, there are situations when there is a great deal of repeated data and, therefore, it may be reasonable to enable these dictionaries in some cases. Thus, as indicated above, text and attribute value dictionaries optionally may be implemented. The added overhead of performing a uniqueness check may be lower than the overhead of copying and caching large amounts of duplicate data, especially when the number of unique alternatives is relatively small.

Certain example embodiments may write partition data directly to the parsed data store bypassing, for example, the local partition pool. This may be advantageous, e.g., if the direct write to the parsed data store is efficient. A Terracotta Big Memory based implementation of the parsed data store may provide direct partition update functionality in a scalable and efficient manner in certain example embodiments.

In certain example implementations, dedicated partitions may be allocated when the system is initialized, and the parsed data store may retain ownership of the partitions through the system lifecycle. This optional approach (rather than allocating partitions on demand) can help to reduce the likelihood of the heap storage that is intended for use by the parser being scavenged by other non-parsing processes. Such scavenging otherwise may sometimes result in over-utilization of heap storage (e.g., by non-parsing processes) and lead to poor overall system performance.

When local partition throttling is used, the parsed data store may limit the amount (e.g., in terms of total size or number) of storage that is committed to cached partitions throughout the system. When a threshold is exceeded, no new documents may be parsed until total usage by all documents drops below the threshold and/or any attempt to allocate a new cacheable partition causing an existing local partition to be archived to the parsed data store may be at least temporarily halted, e.g., until data is moved to backing storage. Tradeoffs may be considered when tuning a system with a high transaction rate, e.g., so as to reduce the overall system impact. For example, partition size, processing power, I/O time, amount of memory, etc., may be taken into consideration when implementing certain example embodiments.

The various characteristics of the system may be used to tune the behavior of as appropriate for a given installation. Example tuning techniques may include, for example:

-   -   Varying the amount of memory allocated for local partitions,         which can reduce the impact of peak document arrival at a server         (e.g., in terms of number and/or size);     -   Dedicated memory allocation for the local partitions, which can         reduce the likelihood of non-parsing threads over-utilizing heap         storage;     -   Using attribute value dictionaries, which can reduce total         memory usage (e.g., local memory and/or cache usage), for         instance, when there is a high incidence of repeated attribute         values;     -   Using text value dictionaries, which can reduce total memory         usage (both local memory and/or cache usage), for instance, when         there is a high incidence of repeated text values; and/or     -   Partition size can be selected. When an environment supports         direct access to the cached store, larger buffer sizes may be         advantageous. When environments do not support direct access to         the cached store, smaller buffer sizes may be advantageous. The         use of shared access with an updater may in some instances         benefit from smaller partition size.

When the user program no longer contains references to the document node or any of its children nodes, the archived buffers in the parsed data store may be released.

Inter-process sharing of a cacheable document may be facilitated in certain example embodiments through serialization of the dictionaries and parser table used by the document to the shared cache (parsed data store). Because the partition table includes no memory references (e.g., addresses) in certain example embodiments, the serialization can be efficient and direct, thereby improving the practical utility of a shared document (for example, in a cloud computing environment). Multiple concurrent readers may be fully supported in this configuration, even when the processes share no physical program memory.

Supporting multiple concurrent readers and one updater is possible, e.g., if the caching hierarchy provides update notifications to the readers when the cache contents change. A parsed data store embodiment using Terracotta's Big Memory, for example, can provide this notification. Change notification may result in the refresh of partitions that have been updated by another process.

A description of certain terms is provided below for aiding in the understanding of how certain example embodiments may operate. However, it is to be understood that the following descriptions are provided by way of example for explanatory purposes and should not be construed as being limiting on the claims, unless expressly noted.

Term Example Description Allocation A potentially linear list maintained in the partition table. For each cacheable Array partition in the partition table, a corresponding entry in the allocation array may specify the allocation identifier of the last encoded node in the partition. This makes it possible, given a node's allocation identifier, to determine the cacheable partition in which a node resides, e.g., by performing a simple linear, binary, or other search of the allocation array. Given a cacheable partition, the exact location of an encoded node in the partition can be calculated by using the node's allocation identifier as a relative offset into the partition's locator array. Allocation When a parsed XML fragment is encoded into a cacheable partition, the Identifier corresponding encoded node may be assigned an allocation identifier. The allocation identifier may preserve the order in which nodes are created. One simple order-preserving allocation identifier that may be used in connection with certain example embodiments is a simple integer counter that is incremented every time a new node is encoded into a cacheable partition. The allocation identifier may be unique within a cacheable document and may be unique for the life of the cached document. Each DOM entity (e.g., element, text, etc.) may be assigned an allocation identifier that allows it to be located within the cached document. As alluded to above, it may be an integer value and assigned in ascending order as XML entities are encountered during parsing. Terracotta A feature that allows large (and possibly serializable) objects to be stored in an Big off-heap memory cache. The use of Terracotta's Big Memory feature may be Memory used in connection with the parsed data store of certain example embodiments. Cacheable A composite object that includes a DOM document node implementation Document along with a partition table and associated cacheable partitions. Any DOM object may be navigated to and retrieved from the cached document. A cacheable document may be created by invoking the XML partitioning engine, passing it an XML document stream and/or a parsed data store implementation. Cacheable A composite object that includes structured information about a segment of an Partition XML document that has been pre-parsed. In certain example embodiments, a cacheable partition may include a character array, a properties array, and a locator array. A cacheable partition may be present in an on-heap local partition pool, and/or it may have been serialized and archived to the parsed data store. Character A component part of a cacheable partition, in certain example embodiments, Array that is available for storing character data. The values of XML text, CData entities, and XML attribute values may be saved in the Character Array. Dictionary A potentially memory-resident storage mechanism that ensures only a single copy of a value is saved. Multiple encoded nodes can refer to the same dictionary entry, thereby reducing memory usage. Element names, attribute names, namespace URIs, and/or the like, may be stored in dictionaries in certain example implementations, thereby potentially reducing the size of encoded nodes. Attribute values and text values may optionally be stored in dictionaries. Direct A technique in which the use of the local partition pool is reduced in favor of Parsed Data direct access to the caching store. This approach may be desirable when, for Access example, access to the caching store is possible without serialization and deserialization overhead. Document A node created by the parsing process. DOM parsers may produce a Node document node from which other members of a DOM tree are retrieved through navigation. The document node created by the XML partitioning engine may also include a names dictionary, a namespace dictionary, and a reference to a partition table. DOM Hardware and/of program code that takes an allocation identifier reference to Instantiator an encoded DOM node in a cacheable partition and instantiates (creates) a fully functioning DOM node. Encoded Information in a cacheable partition that allows the corresponding DOM node Node Entry to be constructed on demand. Every encoded node entry in a cacheable partition may be assigned a unique allocation identifier that is used for subsequent retrieval of the encoded node Familial Each node in a DOM tree may have a parent node, a previous sibling, and a Relationship next sibling. Additionally, an element node may have a first child and a last child. When nodes are encoded in the properties array, the references to these familial nodes may be recorded as allocation identifiers rather than physical memory references. Inter-Node A reference using a node's allocation identifier. Encoded DOM nodes may Reference represent familial references (e.g., parent, sibling, and/or child references) using allocation identifiers. In order to construct the DOM node given its allocation identifier, an index maintained by the partition table may determine which cacheable partition includes the encode node. This may be achieved without fetching any archived buffers in certain example embodiments. Having identified the cacheable partition containing the node, the allocation identifier may be used as an offset into the locator array to determine the starting position of the encoded node's definition in the properties array. The values in the properties may be indices into the character array, or into the names or namespace tables, and may allow the full construction of the requested node. Local A pool of cacheable partitions that has not been archived to the parsed data Partition store and, thus, may be immediately usable. It is possible and potentially Pool likely that many XML documents may not require any serialization and can reside entirely in the local partition pool. Local A technique that maintains total memory allocation for the local partition pool Partition below some threshold. If the threshold is exceeded, additional document Pool parsing may be delayed until the allocation drops below the threshold. Throttling Locator A component part of a cacheable partition, in certain example embodiments. Array It may be a linear list, e.g., of integers, that identifies the starting position of an encoded node in the properties array and thus may represent a list of the encoded nodes in the order that they were created. Parsed Data A non-program-memory storage mechanism (e.g., off-heap memory in Java- Store based environments) where cacheable partitions can be archived for subsequent retrieval and use. One possible embodiment of such off-heap storage is a Terracotta Big Memory instance, which may be multi-tiered and include non-heap local memory (Terracotta Big Memory), as well as traditional disk-based (and potentially shared) caches. Partition A collection of cacheable partitions that includes the metadata from the pre- Table parsing of an XML document stream. It also may include an allocation array that allows a node's allocation identifier to be resolved to a specific cacheable partition. It may be created by the XML partitioning engine. Properties A component part of a Cacheable Partition that is an array of integer or other Array values, in certain example embodiments. The information that defines an encoded DOM node may be represented as a series of integer or other values somewhere within the properties array, potentially adjacent to other DOM node encodings. The integer values may encode the node type, references to familial nodes (e.g., parents, siblings, and/or children) using allocation identifiers, and offsets into the character array for attribute and text values. XML A stream of characters that comprises a well-formed XML document. Document Stream XML Hardware and/or a software program that pre-parses an XML document steam Partitioning into fragments and creates a cacheable document including, for example, a Engine partition table and one or more cacheable partitions. The pre-parsed fragments may be stored in cacheable partitions and/or in dictionaries.

Although certain example embodiments have been described in connection with XML documents and the Document Object Model, it will be appreciated that the example techniques described herein may be used in connection with other document and/or model types.

It will be appreciated that as used herein, the terms system, subsystem, service, programmed logic circuitry, and the like may be implemented as any suitable combination of software, hardware, firmware, and/or the like. It also will be appreciated that the storage locations herein may be any suitable combination of disk drive devices, memory locations, solid state drives, CD-ROMs, DVDs, tape backups, storage area network (SAN) systems, and/or any other appropriate tangible computer readable storage medium. It also will be appreciated that the techniques described herein may be accomplished by having a processor execute instructions that may be tangibly stored on a computer readable storage medium.

While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. 

1. A system for processing XML documents, comprising: processing resources including at least one processor, a memory, and a non-transitory computer readable storage medium; wherein the processing resources are configured to: parse an XML document into one or more constituent nodes, the XML document including a plurality of objects representable in accordance with an object model, the XML document being parsed without also instantiating the objects therein; store the parsed constituent nodes and associated metadata in one or more partitions; and in response to requests for objects from the XML document from a user program, instantiate only said requested objects from their associated partition(s) in accordance with the object model.
 2. The system of claim 1, wherein each said cacheable partition includes a locator array, a properties array, and a character array, wherein: the locator array is configured to identify starting positions of nodes encoded in the properties array; and the properties array is configured to store encoded nodes, as well as, for each said encoded node: metadata including a respective node type, reference(s) to any familial nodes thereof, and offset(s) into the character array for any attribute and/or text value(s) associated therewith.
 3. The system of claim 2, wherein each said reference is an allocation identifier.
 4. The system of claim 1, wherein the XML document is parsed by executing a pre-parsing initialization process that includes: creating a cacheable document node that corresponds to the starting point for user program access to the XML document; allocating a name dictionary that includes an indexed entry for each unique XML tag name included in the XML document; allocating a namespace dictionary that includes an index entry for each unique XML namespace included in the XML document; and allocating a partition table that includes a list of cacheable partitions and an allocation array that allows a node's allocation identifier to be resolved to a specific cacheable partition, each said cacheable partition including metadata from the pre-parsing initialization process.
 5. The system of claim 4, wherein the pre-parsing initialization process further includes allocating an attribute value dictionary that stores unique occurrences of attribute values included in the XML document, and/or a text value dictionary that stores unique occurrences of text values included in the XML document.
 6. The system of claim 4, wherein the parsing includes: recognizing parsing events of predefined parsing event types within the XML document; creating nodes for the recognized parsing events; adding the created nodes to a current partition while there is sufficient space therein; and creating a new partition when there is insufficient space in the current partition for adding created nodes, updating the partition table, and continuing with the adding by treating the newly created partition as the current partition.
 7. The system of claim 4, wherein the partitions and the partition table are movable from the memory to the non-transitory computer readable storage medium when moving the XML document from session-to-session.
 8. The system of claim 1, wherein the partitions are movable from the memory to the non-transitory computer readable storage medium when memory usage reaches or exceeds a threshold.
 9. The system of claim 1, wherein the partitions are movable through a caching storage hierarchy of the processing resources without adjusting or encoding memory references therein.
 10. The system of claim 1, wherein objects instantiated from the partitions, when resident in the memory, are free from references to other objects in their own respective partitions and any other partitions.
 11. The system of claim 1, wherein the processing resources are further configured to remove partitions for the parsed document from the memory and/or the non-transitory computer readable storage medium when the user program no longer includes any references to the document or any objects thereof
 12. The system of claim 1, wherein the partitions include only logical references among and between different nodes.
 13. The system of claim 1, wherein the object model is the document object model.
 14. A method of processing large documents, comprising: parsing, in connection with at least one processor, a large document into one or more constituent nodes, the document including a plurality of objects representable in accordance with an object model, the document being parsed without also instantiating the objects therein; storing the parsed constituent nodes and associated metadata in one or more cacheable partitions, the cacheable partitions being located in a memory and/or a non-transitory backing store; and handling a request from a user program for an object from the document by: identifying the partition(s) in which nodes corresponding to the requested object is/are located; instantiating only said requested objects from the identified partition(s) in accordance with the object model, wherein the cacheable partitions are structured to include only logical references among and between different nodes.
 15. The method of claim 14, further comprising, implementing a pre-parsing process prior to said parsing, the pre-parsing process comprising: creating a cacheable document node that corresponds to the starting point for user program access to the XML document; allocating a name dictionary that includes an indexed entry for each unique XML tag name included in the XML document; allocating a namespace dictionary that includes an index entry for each unique XML namespace included in the XML document; and allocating a partition table that includes a list of cacheable partitions and an allocation array that allows a node's allocation identifier to be resolved to a specific cacheable partition, each said cacheable partition including metadata from the pre-parsing process.
 16. The method of claim 15, wherein the pre-parsing process further comprising allocating an attribute value dictionary that stores unique occurrences of attribute values included in the XML document, and/or a text value dictionary that stores unique occurrences of text values included in the XML document.
 17. The method of claim 15, wherein the parsing comprises: recognizing parsing events of predefined parsing event types within the XML document; creating nodes for the recognized parsing events; adding the created nodes to a current partition while there is sufficient space therein; and when there is insufficient space in the current partition for adding created nodes: creating a new partition, updating the partition table, and continuing with the adding by treating the newly created partition as the current partition.
 18. The method of claim 14, wherein each said cacheable partition includes a locator array, a properties array, and a character array, wherein: the locator array is configured to identify starting positions of nodes encoded in the properties array; and the properties array is configured to store encoded nodes, as well as, for each said encoded node: metadata including a respective node type, reference(s) to any familial nodes thereof, and offset(s) into the character array for any attribute and/or text value(s) associated therewith.
 19. The method of claim 14, further comprising moving the partitions through a caching storage hierarchy without adjusting or encoding memory references of the partitions.
 20. The method of claim 14, wherein objects instantiated from the partitions, when resident in the memory, are free from references to other objects in their own respective partitions and any other partitions.
 21. The method of claim 14, further comprising removing partitions for the parsed document from the memory and/or the backing store when the user program no longer includes any references to the document or any objects thereof
 22. The method of claim 14, wherein the document is an XML document and/or the object model is the document object model.
 23. A non-transitory computer readable storage medium tangibly storing instructions that, when implemented, execute at least a method according to claim
 14. 24-38. (canceled) 